Triple

T5834759
Position Surface form Disambiguated ID Type / Status
Subject Rebekah Elmaloglou E129439 entity
Predicate employer P7 FINISHED
Object Nine Network E58646 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Nine Network | Statement: [Rebekah Elmaloglou, employer, Nine Network]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nine Network
Context triple: [Rebekah Elmaloglou, employer, Nine Network]
  • A. Nine Network chosen
    Nine Network is a major Australian commercial television network known for broadcasting popular news, sports, and entertainment programming nationwide.
  • B. Seven Network
    Seven Network is a major Australian commercial free-to-air television network known for broadcasting popular sports, news, and entertainment programming nationwide.
  • C. Network Ten
    Network Ten is a major Australian commercial television network known for broadcasting popular entertainment, news, and sports programming nationwide.
  • D. Foxe Channel
    Foxe Channel is a narrow Arctic waterway in northern Canada that links Foxe Basin with the surrounding polar seas and forms part of the Northwest Passage region.
  • E. TNT network
    TNT network is an American cable television channel known for airing drama series, sports coverage, and feature films.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69c0084af79c81908af128ccc29983d0 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c034a1e6a88190b1aac05511793315 completed March 22, 2026, 6:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0b0ef1c988190b452158b560cf39f completed March 23, 2026, 3:18 a.m.
Created at: March 22, 2026, 3:54 p.m.